if (!require("pacman")) {install.packages("pacman")}
pacman::p_load(DT,
               janitor,
               kableExtra,
               lubridate,
               tidyverse)

options(DT.options = list(dom = "Blfrtip",
                          scrollX = TRUE,
                          pageLength = 5,
                          columnDefs = list(list(targets = '_all', 
                                                 className = 'dt-center')),
                          buttons = c('copy', 'csv', 'excel', 'pdf')))

Methods

ROSES guidelines

read_csv("roses_checklist.csv", 
         locale = locale(encoding = "latin1")) %>%
  datatable(.,
            extensions = "Buttons")

Literature searches

Strings used for literature searches depending on the database, as follows:

Scopus: TITLE-ABS-KEY((“metaanal*” OR “meta-anal*” OR “metaregres*” OR “meta-regres*” OR (quantitative* w/3 review* ) OR (quantitative* w/3 synthe*) OR (global* w/3 synthe*) OR “comprehensive evidence”) AND (“sexual* select*” OR “*male choice” OR “mate cho*” OR “mat* prefer*” OR “*male prefer*” OR “intrasexual competition” OR “intra-sexual competition” OR “intersexual competition” OR “inter-sexual competition” OR “sperm competition” OR “mating pattern*” OR “assortative mating” OR “mating success” OR “polyandr*” OR “polygy*” OR “extra-pair” OR extrapair OR “mate guarding” OR “reproductive tactic*” OR remating OR “honest signal*” OR “sexual signal*” OR “ornament*” OR “sperm transfer” OR “good genes” OR “good-genes” OR “ejaculate trait*” OR “ejaculate production” OR “bird song*” OR “mating strateg*” OR “bateman gradient*”))

Web of Science: TOPIC((“metaanal*” OR “meta-anal*” OR “metaregres*” OR “meta-regres*” OR (quantitativ* NEAR/3 review* ) OR (quantitative* NEAR/3 synthe*) OR (global* NEAR/3 synthe*) OR “comprehensive evidence”) AND (“sexual* select*” OR “*male choice” OR “mate cho*” OR “mat* prefer*” OR “*male prefer*” OR “intrasexual competition” OR “intra-sexual competition” OR “intersexual competition” OR “inter-sexual competition” OR “sperm competition” OR “mating pattern*” OR “assortative mating” OR “mating success” OR “polyandr*” OR “polygy*” OR “extra-pair” OR extrapair OR “mate guarding” OR “reproductive tactic*” OR remating OR “honest signal*” OR “sexual signal*” OR “ornament*” OR “sperm transfer” OR “good genes” OR “good-genes” OR “ejaculate trait*” OR “ejaculate production” OR “bird song*” OR “mating strateg*” OR “bateman gradient*”))

Google Scholar:
Simplified Chinese: ((“荟萃分析” OR “元分析”) AND (“性选择” OR “性别选择”))
Traditional Chinese: ((“薈萃分析” OR “元分析”) AND (“性選擇” OR “性别選擇”))
Croatian: (“meta-analiza” AND (“spolni odabir” OR “seksualna selekcija” OR “spolna selekcija”))
Japanese: ((“メタ分析” OR “メタ解析”) AND “性選択”)
Polish: (“metaanaliza” AND “dobór płciowy”)
Portuguese: (“meta-análise” AND “seleção sexual”)
Russian: (“мета-анализ” AND “половой отбор”)
Spanish: (“meta-análisis” AND “selección sexual”)

Screening

A few studies employed meta-analytical methods but the data they used to make inferences were not from empirical papers (e.g. Friis et al. 2021 used citizen data, Winternitz et al. 2013 used genbank entries, Holman 2016 used simulated data, and Dobson et al. 2018 used estimated data). We therefore deemed these studies invalid according to our criteria for broad meta-analyses and excluded them from our systematic map during full-text screening. Furthermore, we did not consider plasma concentration of carotenoids as a sexual trait, despite evidence of it being connected to the expression of ornaments. As a consequence we excluded Simons et al. (2015) from our systematic map as this study exclusively investigated plasma concentration carotenoids. In contrast, we included both Simons et al. (2012) and Koch et al. (2016) as they explored individual traits that could be considered ornaments (e.g. plumage) in addition to plasma concentration of carotenoids.

read_csv("full_text_screeening_results.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Data extraction

Systematic map

We tried to summarise questions from meta-analytical studies by clumping similar subquestions into a single one, as described for Garamszegi (2005) in section III.6.c. However, if a summarised or original question fitted more than two topics within our classification framework, we would split it into multiple questions as long as authors provided results for each one of them. For instance, Alissa (2020) presented multiple questions with each belonging to a different topic related to sexual selection. Thus, in that case (and others alike) we listed questions separately as the study showed findings that specifically answered them. This system worked well for all but four studies (McLean et al. 2012; Parker 2013; Thornhill & Moller 1998; Yasukawa 2010), which had questions that fitted three different topics (pre-copulatory sexual traits, mate choice, and mating success) but could not be split. This is because they mixed mate choice outcomes with observations of mating success (as others described in section III.5.d), without any form of distinction (e.g. moderator) in their results. Thus, we chose to remove the relevant questions from these four cases from the mate choice category, leaving them to pre-copulatory sexual traits and mating success only. Additionally, we had trouble to classify the study from Garcia-Roa et al. (2020) as it used several different measures to calculate effect sizes. Because many of its effect sizes were related to sperm number and genital traits, we put this study within the topic of post-copulatory intrasexual competition. We also note that some meta-analyses might actually connect different topics but our classification system might have not considered it as such if these links are not central to the study. For instance, studies that evaluate whether mating is assortative consider many traits, including ornaments (e.g. Jiang et al. 2013; Rios Moura 2021; Wang 2019). Yet, because ornaments were not the main focus of these questions, we did not attribute the topic “pre-copulatory sexual traits” to these studies.

read_csv("systematic_map_extraction.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Reporting appraisal

read_csv("reporting_appraisal_extraction.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Results

Systematic map

read_csv("systematic_map_results.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Questions

List

Columns after DOI (except the last one) are the categories we used to classify questions, in which 1 represents that a given question fitted in the category and 0 that it did not. sex_roles_classification refers to the classification regarding its conformity with sex roles depending on the sex focused by the question (see details in Section II.3.a).

read_csv("questions_list.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Questions’ trait modality

Only questions that fitted the pre-copulatory sexual trait category were included here.

read_csv("questions_modality.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Reporting appraisal

read_csv("reporting_appraisal_results.csv") %>% 
  datatable(.,
            extensions = "Buttons")

Bibliometrics

Affiliations

read_csv("bibliometrics_affiliations.csv", 
         locale = locale(encoding = "latin1")) %>%
  datatable(.,
            extensions = "Buttons")

Author gender

This dataset contains, on each row, the name of an author from a meta-analytical study. author_order represents the order in which the name appeared in the authorship list of that study and total_number_authors represents the total number of authors of that study. automated_gender shows the gender assigned to first_name using the package genderizeR, with its certainty as the variable automated_certainty. manual_gender is the revised gender assignment, including manual insertions when certainty from automated process was lower than 0.95.

read_csv("bibliometrics_gender.csv", 
         locale = locale(encoding = "latin1")) %>%
  datatable(.,
            extensions = "Buttons")